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DC Field | Value | Language |
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dc.contributor.author | Makantasis, Konstantinos | - |
dc.contributor.author | Doulamis, Anastasios D. | - |
dc.contributor.author | Doulamis, Nikolaos D. | - |
dc.contributor.author | Nikitakis, Antonis | - |
dc.date.accessioned | 2024-08-20T08:50:08Z | - |
dc.date.available | 2024-08-20T08:50:08Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | Makantasis, K., Doulamis, A. D., Doulamis, N. D., & Nikitakis, A. (2018). Tensor-based classification models for hyperspectral data analysis. IEEE Transactions on Geoscience and Remote Sensing, 56(12), 6884-6898. | en_GB |
dc.identifier.uri | https://www.um.edu.mt/library/oar/handle/123456789/125523 | - |
dc.description.abstract | In this paper, we present tensor-based linear and nonlinear models for hyperspectral data classification and analysis. By exploiting the principles of tensor algebra, we introduce new classification architectures, the weight parameters of which satisfy the rank-1 canonical decomposition property. Then, we propose learning algorithms to train both linear and nonlinear classifiers. The advantages of the proposed classification approach are that: 1) it significantly reduces the number of weight parameters required to train the model (and thus the respective number of training samples); 2) it provides a physical interpretation of model coefficients on the classification output; and 3) it retains the spatial and spectral coherency of the input samples. The linear tensor-based model exploits the principles of logistic regression, assuming the rank-1 canonical decomposition property among its weights. For the nonlinear classifier, we propose a modification of a feedforward neural network (FNN), called rank-1 FNN, since its weights satisfy again the rank-1 canonical decomposition property. An appropriate learning algorithm is also proposed to train the network. Experimental results and comparisons with state-of-the-art classification methods, either linear (e.g., linear support vector machine) or nonlinear (e.g., deep learning), indicate the outperformance of the proposed scheme, especially in the cases where a small number of training samples is available. | en_GB |
dc.language.iso | en | en_GB |
dc.publisher | Institute of Electrical and Electronics Engineers | en_GB |
dc.rights | info:eu-repo/semantics/restrictedAccess | en_GB |
dc.subject | Hyperspectral imaging | en_GB |
dc.subject | Remote-sensing images | en_GB |
dc.subject | Tensor products | en_GB |
dc.subject | Tensor algebra | en_GB |
dc.subject | Neural networks (Computer science) | en_GB |
dc.title | Tensor-based classification models for hyperspectral data analysis | en_GB |
dc.type | article | en_GB |
dc.rights.holder | The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder. | en_GB |
dc.description.reviewed | peer-reviewed | en_GB |
dc.identifier.doi | 10.1109/TGRS.2018.2845450 | - |
dc.publication.title | IEEE Transactions on Geoscience and Remote Sensing | en_GB |
Appears in Collections: | Scholarly Works - FacICTAI |
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Tensor based classification models for hyperspectral data analysis 2018.pdf Restricted Access | 4.36 MB | Adobe PDF | View/Open Request a copy |
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